{"id":"https://openalex.org/W3012187600","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023338","title":"Geometric Discriminant Analysis for I-vector Based Speaker Verification","display_name":"Geometric Discriminant Analysis for I-vector Based Speaker Verification","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3012187600","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023338","mag":"3012187600"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc47483.2019.9023338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101801662","display_name":"Can Xu","orcid":"https://orcid.org/0000-0003-1365-4778"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Can Xu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059065171","display_name":"Xianhong Chen","orcid":"https://orcid.org/0000-0002-5001-0587"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianhong Chen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049944728","display_name":"Liang He","orcid":"https://orcid.org/0000-0003-4076-7479"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang He","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409741","display_name":"Jia Liu","orcid":"https://orcid.org/0000-0003-0383-0934"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20983173,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":null,"first_page":"1636","last_page":"1640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.963699996471405,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.7331677675247192},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7258914113044739},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6757689714431763},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5309120416641235},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5236032605171204},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5195348858833313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5039848685264587},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.4778425395488739},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4388201832771301},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3806496858596802},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2662630081176758}],"concepts":[{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.7331677675247192},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7258914113044739},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6757689714431763},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5309120416641235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5236032605171204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5195348858833313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5039848685264587},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.4778425395488739},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4388201832771301},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3806496858596802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2662630081176758},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc47483.2019.9023338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2026358225","https://openalex.org/W2117513046","https://openalex.org/W2121812409","https://openalex.org/W2134262590","https://openalex.org/W2137611320","https://openalex.org/W2150769028","https://openalex.org/W2164019725","https://openalex.org/W2359521383","https://openalex.org/W2395069802","https://openalex.org/W2395750323","https://openalex.org/W2397703486","https://openalex.org/W2398521502","https://openalex.org/W2406312423","https://openalex.org/W2408021097","https://openalex.org/W2889268328","https://openalex.org/W2890830077","https://openalex.org/W2910350028","https://openalex.org/W2963068250","https://openalex.org/W6687442591","https://openalex.org/W6712094196","https://openalex.org/W6712389868","https://openalex.org/W6713727690"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2362114017","https://openalex.org/W3147024994","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W1978302214","https://openalex.org/W2021817983","https://openalex.org/W3008559849","https://openalex.org/W2371177901"],"abstract_inverted_index":{"Many":[0],"i-vector":[1,135],"based":[2],"speaker":[3,31,91],"verification":[4,92],"use":[5],"linear":[6],"discriminant":[7,95],"analysis":[8,96],"(LDA)":[9],"as":[10],"a":[11],"post-processing":[12],"stage.":[13],"LDA":[14,40,55,159],"maximizes":[15],"the":[16,20,60,76,108,122,142,149],"arithmetic":[17,104],"mean":[18,101,105],"of":[19,27,62,78,103,125,144,153],"Kullback-Leibler":[21],"(KL)":[22],"divergences":[23],"between":[24],"different":[25,63,79,83,129],"pairs":[26],"speakers.":[28],"However,":[29],"for":[30],"verification,":[32],"speakers":[33,64,80,146],"with":[34,71,93,158],"small":[35,51,117],"divergence":[36],"are":[37,65,165],"easily":[38],"misjudged.":[39],"is":[41,172],"not":[42,47],"optimal":[43],"because":[44],"it":[45],"does":[46],"emphasize":[48],"on":[49,115,134],"enlarging":[50,116],"divergences.":[52,110,118],"In":[53],"addition,":[54],"makes":[56],"an":[57],"assumption":[58],"that":[59],"i-vectors":[61],"well":[66],"modeled":[67],"by":[68,86],"Gaussian":[69],"distributions":[70,77],"identical":[72],"class":[73],"covariance.":[74],"Actually,":[75],"can":[81],"have":[82],"covariances.":[84],"Motivated":[85],"these":[87],"observations,":[88],"we":[89,120],"explore":[90],"geometric":[94,100],"(GDA),":[97],"which":[98],"uses":[99],"instead":[102],"when":[106,141,169],"maximizing":[107],"KL":[109],"It":[111],"puts":[112],"more":[113],"emphasis":[114],"Furthermore,":[119],"study":[121],"heteroscedastic":[123],"extension":[124],"GDA":[126,154,162],"(HGDA),":[127],"taking":[128],"covariances":[130],"into":[131],"consideration.":[132],"Experiments":[133],"machine":[136],"learning":[137],"challenge":[138],"indicate":[139],"that,":[140],"number":[143],"training":[145,170],"becomes":[147,160],"smaller,":[148],"relative":[150],"performance":[151],"improvement":[152],"and":[155,163],"HGDA":[156,164],"compared":[157],"larger.":[161],"better":[166],"choices":[167],"especially":[168],"data":[171],"limited.":[173]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
